Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-26T03:11:57.475Z Has data issue: false hasContentIssue false

Using Estimates of Weed Pressure to Establish Crop Yield Loss Equations

Published online by Cambridge University Press:  12 June 2017

R. Gordon Harvey
Affiliation:
Dep. Agron., Univ. Wisconsin, Madison, WI 53706
Clark R. Wagner
Affiliation:
Dep. Agron., Univ. Wisconsin, Madison, WI 53706

Abstract

Herbicide efficacy trials in field corn, sweet corn, and soybean were conducted at three locations in Wisconsin over a 6-yr period. Percent weed pressure (WP) was determined by visually estimating the contribution of all weed species present to the total crop and weed volume in each plot. Crop yields in each plot were measured. Percent crop yield reduction (YLDRED) was calculated by comparing mean yields of individual treatments with those of the highest yielding treatment in each trial. Linear regression analyses of YLDRED and WP data from 1640 field corn and 138 sweet corn treatments were significant. Nonlinear regression analysis of YLDRED and WP data from all 1374 soybean treatments was significant; however, a linear regression of those 1154 soybean treatments with WP ratings of 30 or less produced a more easily interpreted regression equation.

Type
Research
Copyright
Copyright © 1994 by the Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

1. Bauer, T. A., Mortensen, D. A., Wicks, G. A., Hayden, T. A., and Martin, A. R. 1991. Environmental variability associated with economic thresholds for soybeans. Weed Sci. 39:564569.Google Scholar
2. Beckett, T. H., Stoller, E. W., and Wax, L. M. 1988. Interference of four annual weeds in corn (Zea mays). Weed Sci. 36:764769.CrossRefGoogle Scholar
3. Bussler, B. H. and Maxwell, B. D. 1993. Utilizing plant volume measurements to assess competition in corn neighborhoods. Weed Sci. Soc. Amer. Abstr. 33:53.Google Scholar
4. Chisaka, H. 1977. Weed damage to crops: yield loss due to weed interference. P. 116 in Integrated Control of Weeds, Fryer, J. D. and Matsunaka, S., eds. Univ. of Tokyo Press, Tokyo.Google Scholar
5. Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol. 6:191195.Google Scholar
6. Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239252.CrossRefGoogle Scholar
7. Fellows, G. M. and Roeth, F. W. 1992. Shattercane (Sorghum bicolor) interference in soybean (Glycine max). Weed Sci. 40:6873.Google Scholar
8. Martin, A. R., Mortensen, D. A., and Meyer, G. E. 1993. Visual and photographic assessment of herbicide efficacy trials for use in bioeconomic modeling. Weed Sci. Soc. Amer. Abstr. 33:53.Google Scholar
9. Mortensen, D. A. and Coble, H. D. 1989. The influence of soil water content on common cocklebur (Xanthium strumarium) interference in soybeans (Glycine max). Weed Sci. 37:7683.Google Scholar
10. Norris, R. F. 1993. Case history for weed competition/population ecology: Barnyardgrass (Echinochloa crus-galli) in sugarbeets (Beta vulgaris). Weed Technol. 6:220227.Google Scholar
11. Salisbury, C. D. 1993. Canopy composition as a predictor of crop yield reduction by weeds. Weed Sci. Soc. Amer. Abstr. 33:52.Google Scholar
12. Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: Decision model for postemergence weed control in soybean. Agron. J. 83:413417.Google Scholar